Taskmaster is an AI-powered task management system designed specifically for an AI-driven development workflow. It is a tool that helps streamline and automate coding tasks by leveraging different AI models in a structured way.

Taskmaster is particularly useful when working with various cloud models, as well as within your code editor (e.g., Visual Studio Code, Cursor) and other AI coding agents. It helps optimize your development workflow by:

• Splitting work across models to avoid hitting context limits or model crashes
• Keeping your codebase lean and optimized
• Reducing token expenditure and minimizing errors

Taskmaster can be integrated into your development process in two ways:

1. MCP (Model Context Protocol) Server: You can run Taskmaster as an MCP server within your code editor, such as Cursor or Visual Studio Code. This allows you to configure the AI models you want to use and easily access Taskmaster's features.

2. Command-Line Interface (CLI): You can install Taskmaster globally or within a specific project and use it directly from the command line. This provides a more flexible and portable way to leverage Taskmaster's capabilities.

Regardless of the integration method, Taskmaster helps you streamline your AI-driven development workflow by automating tasks, managing context, and optimizing your token usage. It's a powerful tool that can significantly improve your productivity and the quality of your AI-generated code.

There are two ways to get started with Taskmaster, the AI-powered task management system:

1. MCP (Model Context Protocol) Server:

   • You can easily install Taskmaster as an MCP server and run it within your editor, such as Cursor, Windsurfer, or VS Code.
   • In your editor's MCP section, you can add the Taskmaster server configuration and specify the API keys for the main, research, and fallback models.
   • Once configured, you can initialize Taskmaster AI within your project's AI chat pane.

2. CLI (Command-Line Interface):

   • You can install Taskmaster globally using the npm install command, which will affect every project and file.
   • Alternatively, you can initialize Taskmaster within a specific project using the provided initialization command.
   • After installation, Taskmaster will create an .env file where you can configure the API keys for the main, research, and fallback models.
   • You can then use the task manager parse command to generate a task list from a provided Product Requirement Description (PRD) file.
   • Taskmaster will split the tasks into subtasks, ensuring that your AI models don't hit any context limits or experience crashes.
   • You can then implement the generated tasks within your coding agent, such as Root Code, by providing the task context and having the AI execute them one by one.

Both the MCP and CLI approaches allow you to leverage Taskmaster's capabilities to streamline your AI-driven development workflow, optimize token expenditure, and minimize errors.

To configure Taskmaster with appropriate AI models, follow these steps:

1. When initializing Taskmaster, you will be prompted to customize and configure the AI models. You can add three types of models:

   • Main Model
   • Research Model
   • Fallback Model

2. For the Main Model, it is recommended to use Anthropic's API key due to its low context window.

3. For the Research Model, you can use a Gemini model, such as Gemini 2.5 Pro.

4. As a Fallback Model, you can use a Gemini 2.5 Flash model.

5. Taskmaster also allows you to use custom OpenAI or Anthropic models as alternatives.

6. Make sure to provide the correct API keys for each model provider in the .env file or the MCP configuration.

7. After configuring the models, you can validate the API keys and the configured models by checking the output in the terminal.

8. With the appropriate AI models set up, Taskmaster can effectively split the work across models and keep the development workflow lean and optimized, avoiding context limits and model crashes.

Taskmaster is an AI-powered task management system designed specifically for AI-driven development workflows. It helps streamline and automate coding tasks by leveraging different AI models in a structured way, optimizing your development process.

Key features of Taskmaster:

• Splits work across models: Taskmaster smartly splits tasks across multiple AI models to avoid context limits and model crashes.
• Keeps everything lean and optimized: By leveraging different models efficiently, Taskmaster helps reduce token expenditure and maintain a lean workflow.
• Integrates with various AI coding agents: Taskmaster can be used as an MCP (Model Context Protocol) server or a command-line tool, allowing integration with tools like Cursor, VS Code, and more.
• Automates task generation: Taskmaster can parse project requirements and automatically generate a structured task list, breaking down complex tasks into manageable subtasks.
• Provides task management capabilities: The generated tasks can be easily managed within Taskmaster, including features like status tracking, notes, and more.

To get started with Taskmaster, you can either set it up as an MCP server or install it as a command-line tool. The MCP setup allows you to integrate Taskmaster directly within your coding environment, while the command-line approach provides a more standalone solution.

Regardless of the approach, Taskmaster makes it easy to leverage the power of AI in your development workflow, helping you save time, reduce errors, and optimize your token usage.

To implement the tasks and build the Task Flow app, follow these steps:

1. After generating the task list using the task manager parse pro command, you can go into your coding agent like Visual Studio Code and provide the context for the tasks.

2. In your coding agent, say "Implement all of the tasks one by one" to focus on executing the tasks.

3. The AI will then work on implementing the tasks, one by one, to build the Task Flow app.

4. You can see the quick note tab where you can easily add a new note, task, and set its status (to-do, in progress, review, completed).

5. You can also add a voice note, which will be transcribed by the AI.

6. The calendar and timeline components are already coded and working.

7. The app also includes features like notifications, automations, and the ability to add new tasks with different colors.

8. Overall, the AI has done a decent job in rapidly coding out the various components of the Task Flow app, without any major errors.

Taskmaster is an AI-powered task management system designed specifically for an AI-driven development workflow. It helps streamline and automate coding tasks by leveraging different AI models in a structured way, optimizing your development workflow and saving you time, reducing errors, and minimizing token expenditure.

Taskmaster can be easily integrated into your development environment, whether you're using Cursor, VS Code, or any other IDE. It provides two options for setup - as an MCP (Model Context Protocol) server or as a command-line tool. Both options allow you to configure the AI models you want to use, including the main, research, and fallback models.

By splitting your coding tasks into subtasks, Taskmaster helps you avoid hitting context limits and ensures your AI models can work efficiently without crashing or overloading. It also provides additional features, such as the ability to generate task lists from product requirement descriptions (PRDs) and to implement the tasks directly within your coding environment.

Overall, Taskmaster is a powerful tool that can significantly improve your AI-driven development workflow, helping you save time, reduce errors, and optimize your token expenditure.